96574b47a58b0f3dc4a5b085086ca07a173d432b,scripts/2015_nips_paper/score_metalearning.py,,main,#Any#Any#Any#Any#Any#,13

Before Change

    automl = AutoSklearnClassifier(**automl_arguments)
    print(automl._automl._metadata_directory)
    print("////////")
    return

    automl.fit(X_train, y_train,
               dataset_name=str(task_id),
               X_test=X_test, y_test=y_test,

After Change

        "multiclass": "multiclass.classification",
    }
    task_type = type_of_target(y_train)
    metadata_for_this_task = os.path.abspath(
        os.path.join(working_directory,
                     "metadata_%i/balanced_accuracy_%s_sparse" % (task_id, TASK_TYPES_TO_STRING[task_type])))
    // how to check if data is sparse before running?

    configuration_output_dir = os.path.join(working_directory, str(seed))
    tmp_dir = os.path.join(configuration_output_dir, str(task_id))

Instances